AUTHOR=Meor Yahaya Maizan Syamimi , Teo Jason 

TITLE=Data augmentation using generative adversarial networks for images and biomarkers in medicine and neuroscience

JOURNAL=Frontiers in Applied Mathematics and Statistics

VOLUME=Volume 9 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2023.1162760

DOI=10.3389/fams.2023.1162760

ISSN=2297-4687

ABSTRACT=The field of medicine and neuroscience often faces challenges in obtaining sufficient amounts of diverse data for training machine learning models. Data augmentation can alleviate this issue by artificially synthesising new data from existing data. Generative Adversarial Networks (GANs) provide a promising approach for data augmentation in the context of images and biomarkers. GANs can synthesise high-quality, diverse, and realistic data that can supplement real data in the training process. This paper provides an overview of the use of GANs for data augmentation in medicine and neuroscience. The strengths and weaknesses of various GAN models, including Deep Convolutional GANs (DCGANs) and Wasserstein GANs (WGANs), are discussed. The paper also explores the challenges and ways to address them when using GANs for data augmentation in the field of medicine and neuroscience. The future works on this topic are also discussed.